(a) Field of the Invention
The present invention generally relates to target detection and tracking, and more specifically to a search method for target detection and tracking using multiple angle-only sensors.
(b) Description of Related Art
Historically, electronic tracking systems were able to compute and track the coordinates of a target by measuring its angle of approach and its range. However, in many instances, only the approach angle of the target is available because the range information is often electronically jammed.
Ideally, given at least two sets of approach angle data per target, it is possible to determine the location of the targets. The target location may be determined, for example, by the intersection of the angle data from a pair of sensors. The difficulty with using angle-only methods is that associating bearing lines (i.e., imaginary lines drawn between sensors and targets) with multiple targets requires a substantial search space which may be on the order of (N|).sup.S-1, where N is the number of targets and S is the number of sensors in the system. Locating and tracking targets within the search space requires a significant amount of time and computing power.
A number of methods for solving the above-referenced locating and tracking problem have been proposed. One method, known generally as a multiple elastic feature network (MEFN), associates a number of intersection points of bearing lines to locate and track targets. This is accomplished with N independent sets of M feature specific neurons, or artificial intelligence elements, that respond to the temporal properties of the targets. This method only functions as a two dimensional abstraction of a three dimensional system, using few (approx. three) sensors.
Pattipati et al., "A New Relaxation Algorithm and Passive Sensor Data Association," IEEE Transactions on Automatic Control, 37(2) 1992 pp. 198-213, discloses an approach based on optimization of the log likelihood function using the Lagarangian relaxation method. This method has similar computational complexity to the MEFN method described above, however it is only used for target detection and not for target tracking.
Accordingly, there is a need for a method of rapidly and efficiently detecting and tracking targets in three dimensional space using numerous sensors.